scholarly journals A fisheries acoustic multi-frequency indicator to inform on large scale spatial patterns of aquatic pelagic ecosystems

2013 ◽  
Vol 30 ◽  
pp. 72-79 ◽  
Author(s):  
Verena M. Trenkel ◽  
Laurent Berger
Keyword(s):  
Spatial Patterns ◽  
Large Scale ◽  
Pelagic Ecosystems

scholarly journals Evergreen Plants in Roman Britain and Beyond: Movement, Meaning and Materiality

Britannia ◽  
2017 ◽  
Vol 48 ◽  
pp. 135-173 ◽  
Author(s):  
Lisa A. Lodwick
Keyword(s):  
Material Culture ◽  
Norway Spruce ◽  
Spatial Patterns ◽  
Large Scale ◽  
Food Plants ◽  
Stone Pine ◽  
Roman Britain ◽  
Roman World

ABSTRACTIn tandem with the large-scale translocation of food plants in the Roman world, ornamental evergreen plants and plant items were also introduced to new areas for ritual and ornamental purposes. The extent to which these new plants, primarily box and stone-pine, were grown in Britain has yet to be established. This paper presents a synthesis of archaeobotanical records of box, stone-pine and norway spruce in Roman Britain, highlighting chronological and spatial patterns. Archaeobotanical evidence is used alongside material culture to evaluate the movement of these plants and plant items into Roman Britain, their meaning and materiality in the context of human-plant relations in ornamental gardens and ritual activities. Archaeobotanical evidence for ornamental evergreen plants elsewhere in the Roman world is presented.

scholarly journals Evaluation of modeled global carbon dynamics: analysis based on global carbon flux and above-ground biomass data

2016 ◽  
Author(s):  
Bao-Lin Xue ◽  
Qinghua Guo ◽  
Tianyu Hu ◽  
Yongcai Wang ◽  
Shengli Tao ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Carbon Flux ◽  
Large Scale ◽  
Gross Primary Production ◽  
Carbon Dynamics ◽  
Global Scale ◽  
Above Ground Biomass ◽  
Carbon Cycles ◽  
Ground Biomass ◽  
Global Carbon

Abstract. Dynamic global vegetation models are useful tools for the simulation of carbon dynamics on regional and global scales. However, even the most validated models are usually hampered by the poor availability of global biomass data in the model validation, especially on regional/global scales. Here, taking the integrated biosphere simulator model (IBIS) as an example, we evaluated the modeled carbon dynamics, including gross primary production (GPP) and potential above-ground biomass (AGB), on the global scale. The IBIS model was constrained by both in situ GPP and plot-level AGB data collected from the literature. Independent validation showed that IBIS could reproduce GPP and evapotranspiration with acceptable accuracy at site and global levels. On the global scale, the IBIS-simulated total AGB was similar to those obtained in other studies. However, discrepancies were observed between the model-derived and observed spatial patterns of AGB for Amazonian forests. The differences among the AGB spatial patterns were mainly caused by the single-parameter set of the model used. This study showed that different meteorological inputs can also introduce substantial differences in AGB on the global scale. Further analysis showed that this difference is small compared with parameter-induced differences. The conclusions of our research highlight the necessity of considering the heterogeneity of key model physiological parameters in modeling global AGB. The research also shows that to simulate large-scale carbon dynamics, both carbon flux and AGB data are necessary to constrain the model. The main conclusions of our research will help to improve model simulations of global carbon cycles.

scholarly journals Amplification and Suppression of Distinct Brain-wide Activity Patterns by Catecholamines

2018 ◽  
Author(s):  
RL van den Brink ◽  
S Nieuwenhuis ◽  
TH Donner
Keyword(s):  
Spatial Distribution ◽  
Human Brain ◽  
Spatial Patterns ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Heterogeneous Distribution ◽  
Neurotransmitter Systems ◽  
The Brain ◽  
Catecholamine Levels

ABSTRACTThe widely projecting catecholaminergic (norepinephrine and dopamine) neurotransmitter systems profoundly shape the state of neuronal networks in the forebrain. Current models posit that the effects of catecholaminergic modulation on network dynamics are homogenous across the brain. However, the brain is equipped with a variety of catecholamine receptors with distinct functional effects and heterogeneous density across brain regions. Consequently, catecholaminergic effects on brain-wide network dynamics might be more spatially specific than assumed. We tested this idea through the analysis of functional magnetic resonance imaging (fMRI) measurements performed in humans (19 females, 5 males) at ‘rest’ under pharmacological (atomoxetine-induced) elevation of catecholamine levels. We used a linear decomposition technique to identify spatial patterns of correlated fMRI signal fluctuations that were either increased or decreased by atomoxetine. This yielded two distinct spatial patterns, each expressing reliable and specific drug effects. The spatial structure of both fluctuation patterns resembled the spatial distribution of the expression of catecholamine receptor genes: α1 norepinephrine receptors (for the fluctuation pattern: placebo > atomoxetine), ‘D2-like’ dopamine receptors (pattern: atomoxetine > placebo), and β norepinephrine receptors (for both patterns, with correlations of opposite sign). We conclude that catecholaminergic effects on the forebrain are spatially more structured than traditionally assumed and at least in part explained by the heterogeneous distribution of various catecholamine receptors. Our findings link catecholaminergic effects on large-scale brain networks to low-level characteristics of the underlying neurotransmitter systems. They also provide key constraints for the development of realistic models of neuromodulatory effects on large-scale brain network dynamics.SIGNIFICANCE STATEMENTThe catecholamines norepinephrine and dopamine are an important class of modulatory neurotransmitters. Because of the widespread and diffuse release of these neuromodulators, it has commonly been assumed that their effects on neural interactions are homogenous across the brain. Here, we present results from the human brain that challenge this view. We pharmacologically increased catecholamine levels and imaged the effects on the spontaneous covariations between brain-wide fMRI signals at ‘rest’. We identified two distinct spatial patterns of covariations: one that was amplified and another that was suppressed by catecholamines. Each pattern was associated with the heterogeneous spatial distribution of the expression of distinct catecholamine receptor genes. Our results provide novel insights into the catecholaminergic modulation of large-scale human brain dynamics.

Hydrological response of a large-scale mountainous watershed to rainstorm spatial patterns and reforestation in subtropical China

2018 ◽  
Vol 645 ◽  
pp. 1083-1093 ◽  
Author(s):  
X. Huang ◽  
N.F. Fang ◽  
T.X. Zhu ◽  
L. Wang ◽  
Z.H. Shi ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Large Scale ◽  
Hydrological Response ◽  
Subtropical China ◽  
Mountainous Watershed

Are large scale models useful? A case of nested model domains for assessing current and future stream runoff and sediments

2021 ◽  
Author(s):  
Alena Bartosova ◽  
Berit Arheimer ◽  
Alban de Lavenne ◽  
René Capell ◽  
Johan Strömqvist
Keyword(s):  
Climate Change ◽  
Spatial Patterns ◽  
Large Scale ◽  
Aridity Index ◽  
Global Climate Models ◽  
Hydrological Models ◽  
Nested Model ◽  
Nested Models ◽  
Changing Climate ◽  
Scale Models

<p>Continental and global dynamic hydrological models have emerged recently as tools for e.g. flood forecasting, large-scale climate impact analyses, and estimation of time-dynamic water fluxes into sea basins. One such tool is a dynamic process-based rainfall-runoff and water quality model Hydrological Predictions for Environment (HYPE). We present and compare historical simulations of runoff, soil moisture, aridity, and sediment concentrations for three nested model domains using global, continental (Europe), and national (Sweden) catchment-based HYPE applications. Future impacts on hydrological variables from changing climate were then assessed using the global and continental HYPE applications with ensembles based on 3 CMIP5 global climate models (GCMs).</p><p>Simulated historical sediment concentrations varied considerably among the nested models in spatial patterns while runoff values were more similar. Regardless of the variation, the global model was able to provide information on climate change impacts comparable to those from the continental and national models for hydrological indicators. Output variables that were calibrated, e.g. runoff, were shown to result in more reliable and consistent projected changes among the different model scales than derived variables such as the actual aridity index. The comparison was carried out for ensemble averages as well as individual GCMs to illustrate the variability and the need for robust assessments.</p><p>Global hydrological models are shown to be valuable tools for e.g. first screenings of climate change effects and detection of spatial patterns and can be useful to provide information on current and future hydrological states at various domains. The challenge is (1) in deciding when we should use the large-scale models and (2) in interpreting the results, considering the uncertainty of the model results and quality of data especially at the global scale. Comparison across nested domains demonstrates the significance of scale which needs to be considered when interpreting the impacts alongside with model performance.</p><p>Bartosova et al, 2021: Large-scale hydrological and sediment modeling in nested domains under current and changing climate. Accepted to Special Issue Journal of Hydraulic Engineering.</p>

Mining Spatial Patterns of Distribution of Uranium in Surface and Ground Waters in Ukraine

2017 ◽  
pp. 520-546
Author(s):  
Michael Govorov ◽  
Viktor Putrenko ◽  
Gennady Gienko
Keyword(s):  
Factor Analysis ◽  
Spatial Distribution ◽  
Spatial Patterns ◽  
Large Scale ◽  
Chemical Elements ◽  
Environmental Indicators ◽  
Local Factor ◽  
Correlation And Regression Analysis ◽  
Weighted Factor ◽  
Major Factors

A variety of geovisualization and spatial statistical methods can reveal spatial patterns in the distribution of chemical elements in surface and groundwater, and also identify major factors which define those patterns. This chapter describes a combination of modeling techniques to enhance understanding of large-scale spatial distribution of uranium in groundwater in Ukraine, by linking spatial patterns of several indicators and predictors. Factor, correlation, and regression analysis, including their spatial implementations, were used to describe the impacts of several environmental variables on spatial distribution of uranium. Local factor analysis (or Geographically Weighted Factor Analysis, GWFA) was proposed to identify major environmental factors which define the distribution of uranium, and to discover and map their spatial relationships. The study resulted in a series of maps to help visualize and explore the relationships between uranium and several environmental indicators.

scholarly journals A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images

Biostatistics ◽  
2018 ◽  
Vol 20 (4) ◽  
pp. 565-581
Author(s):  
Qiwei Li ◽  
Xinlei Wang ◽  
Faming Liang ◽  
Faliu Yi ◽  
Yang Xie ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Spatial Patterns ◽  
Cancer Progression ◽  
Large Scale ◽  
Digital Pathology ◽  
Interaction Strength ◽  
Monte Carlo Sampling ◽  
Square Lattice ◽  
Different Types ◽  
Pathology Image

Summary Digital pathology imaging of tumor tissues, which captures histological details in high resolution, is fast becoming a routine clinical procedure. Recent developments in deep-learning methods have enabled the identification, characterization, and classification of individual cells from pathology images analysis at a large scale. This creates new opportunities to study the spatial patterns of and interactions among different types of cells. Reliable statistical approaches to modeling such spatial patterns and interactions can provide insight into tumor progression and shed light on the biological mechanisms of cancer. In this article, we consider the problem of modeling a pathology image with irregular locations of three different types of cells: lymphocyte, stromal, and tumor cells. We propose a novel Bayesian hierarchical model, which incorporates a hidden Potts model to project the irregularly distributed cells to a square lattice and a Markov random field prior model to identify regions in a heterogeneous pathology image. The model allows us to quantify the interactions between different types of cells, some of which are clinically meaningful. We use Markov chain Monte Carlo sampling techniques, combined with a double Metropolis–Hastings algorithm, in order to simulate samples approximately from a distribution with an intractable normalizing constant. The proposed model was applied to the pathology images of $205$ lung cancer patients from the National Lung Screening trial, and the results show that the interaction strength between tumor and stromal cells predicts patient prognosis (P = $0.005$). This statistical methodology provides a new perspective for understanding the role of cell–cell interactions in cancer progression.

scholarly journals Space and time predictions of schistosomiasis snail host population dynamics across hydrologic regimes in Burkina Faso

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Javier Perez-Saez ◽  
Theophile Mande ◽  
Andrea Rinaldo
Keyword(s):  
Population Dynamics ◽  
Burkina Faso ◽  
Spatial Patterns ◽  
Large Scale ◽  
Remotely Sensed ◽  
Host Population ◽  
Snail Host ◽  
Intermediate Hosts ◽  
National Scale ◽  
Temperature And Precipitation

The ecology of the aquatic snails that serve as obligatory intermediate hosts of human schistosomiasis is driven by climatic and hydrological factors which result in specific spatial patterns of occurrence and abundance. These patterns in turn affect, jointly with other determinants, the geography of the disease and the timing of transmission windows, with direct implications for the success of control and elimination programmes in the endemic countries. We address the spatial distribution of the intermediate hosts and their seasonal population dynamics within a predictive ecohydrological framework developed at the national scale for Burkina Faso, West Africa. The approach blends river network-wide information on hydrological ephemerality which conditions snail habitat suitability together with ensembles of discrete time ecological models forced by remotely sensed estimates of temperature and precipitation. The models were validated against up to four years of monthly snail abundance data. Simulations of model ensembles accounting for the uncertainty in remotely sensed products adequately reproduce observed snail demographic fluctuations observed in the field across habitat types, and produce national scale predictions by accounting for spatial patterns of hydrological conditions in the country. Geospatial estimates of seasonal snail abundance underpin large-scale, spatially explicit predictions of schistosomiasis incidence. This work can therefore contribute to the development of disease control and elimination programmes.

Spatial patterns of Posidonia oceanica structural and functional features in the Eastern Mediterranean (Aegean and E. Ionian Seas) in relation to large-scale environmental factors

2020 ◽  
pp. 105222
Author(s):  
Vasilis Gerakaris ◽  
Vasillis Papathanasiou ◽  
Maria Salomidi ◽  
Yiannis Issaris ◽  
Panayotis Panayotidis
Keyword(s):  
Environmental Factors ◽  
Spatial Patterns ◽  
Large Scale ◽  
Eastern Mediterranean ◽  
Posidonia Oceanica ◽  
Functional Features
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